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Database Systems: Design,
Implementation, and
Management
Ninth Edition
Chapter 13
Business Intelligence and Data
Warehouses
Objectives
In this chapter, you will learn:
• How business intelligence is a comprehensive
framework to support business decision making
• How operational data and decision support data
differ
• What a data warehouse is, how to prepare data
for one, and how to implement one
• What star schemas are and how they are
constructed
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Objectives (cont’d.):
• What data mining is and what role it plays in
decision support
• About online analytical processing (OLAP)
• How SQL extensions are used to support
OLAP-type data manipulations
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The Need for Data Analysis
• Managers track daily transactions to evaluate
how the business is performing
• Strategies should be developed to meet
organizational goals using operational
databases
• Data analysis provides information about shortterm tactical evaluations and strategies
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Business Intelligence
• Comprehensive, cohesive, integrated tools and
processes
– Capture, collect, integrate, store, and analyze
data
– Generate information to support business
decision making
• Framework that allows a business to transform:
– Data into information
– Information into knowledge
– Knowledge into wisdom
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Business Intelligence Architecture
• Composed of data, people, processes,
technology, and management of components
• Focuses on strategic and tactical use of
information
• Key performance indicators (KPI)
– Measurements that assess company’s
effectiveness or success in reaching goals
• Multiple tools from different vendors can be
integrated into a single BI framework
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Decision Support Data
• BI effectiveness depends on quality of data
gathered at operational level
• Operational data seldom well-suited for
decision support tasks
• Need reformat data in order to be useful for
business intelligence
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Operational Data vs.
Decision Support Data
• Operational data
– Mostly stored in relational database
– Optimized to support transactions representing
daily operations
• Decision support data differs from operational
data in three main areas:
– Time span
– Granularity
– Dimensionality
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Decision Support
Database Requirements
• Specialized DBMS tailored to provide fast
answers to complex queries
• Four main requirements:
–
–
–
–
Database schema
Data extraction and loading
End-user analytical interface
Database size
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Decision Support
Database Requirements (cont’d.)
• Database schema
– Complex data representations
– Aggregated and summarized data
– Queries extract multidimensional time slices
• Data extraction and filtering
– Supports different data sources
• Flat files
• Hierarchical, network, and relational databases
• Multiple vendors
– Checking for inconsistent data
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Decision Support
Database Requirements (cont’d.)
• End-user analytical interface
– One of most critical DSS DBMS components
– Permits user to navigate through data to simplify
and accelerate decision-making process
• Database size
– In 2005, Wal-Mart had 260 terabytes of data in
its data warehouses
– DBMS must support very large databases
(VLDBs)
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The Data Warehouse
• Integrated, subject-oriented, time-variant, and
nonvolatile collection of data
– Provides support for decision making
• Usually a read-only database optimized for data
analysis and query processing
• Requires time, money, and considerable
managerial effort to create
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The Data Warehouse (cont’d.)
• Data mart
– Small, single-subject data warehouse subset
– More manageable data set than data warehouse
– Provides decision support to small group of
people
– Typically lower cost and lower implementation
time than data warehouse
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Twelve Rules That Define
a Data Warehouse
• Data warehouse and operational environments
are separated
• Data warehouse data are integrated
• Data warehouse contains historical data over
long time
• Data warehouse data are snapshot data
captured at given point in time
• Data warehouse data are subject-oriented
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Twelve Rules That Define
a Data Warehouse (cont’d.)
• Data warehouse data are mainly read-only
– Periodic batch updates from operational data
– No online updates allowed
• Data warehouse development life cycle differs
from classical systems development
• Data warehouse contains data with several
levels of detail:
– Current detail data, old detail data, lightly
summarized data, and highly summarized data
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Twelve Rules That Define
a Data Warehouse (cont’d.)
• Read-only transactions to very large data sets
• Data warehouse environment traces data
sources, transformations, and storage
• Data warehouse’s metadata are critical
component of this environment
• Data warehouse contains chargeback
mechanism for resource usage
– Enforces optimal use of data by end users
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Decision Support Architectural Styles
• Provide advanced decision support features
• Some capable of providing access to
multidimensional data analysis
• Complete data warehouse architecture
supports:
– Decision support data store
– Data extraction and integration filter
– Specialized presentation interface
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Online Analytical Processing
• Advanced data analysis environment that
supports:
– Decision making
– Business modeling
– Operations research
• Four main characteristics:
–
–
–
–
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
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Multidimensional Data Analysis
Techniques
• Data are processed and viewed as part of a
multidimensional structure
• Augmented by the following functions:
– Advanced data presentation functions
– Advanced data aggregation, consolidation, and
classification functions
– Advanced computational functions
– Advanced data modeling functions
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Advanced Database Support
• Advanced data access features include:
– Access to many different kinds of DBMSs, flat
files, and internal and external data sources
– Access to aggregated data warehouse data
– Advanced data navigation
– Rapid and consistent query response times
– Maps end-user requests to appropriate data
source and to proper data access language
– Support for very large databases
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Easy-to-Use End-User Interface
• Advanced OLAP features are more useful when
access is simple
• Many interface features are “borrowed” from
previous generations of data analysis tools
– Already familiar to end users
– Makes OLAP easily accepted and readily used
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Client/Server Architecture
• Provides framework for design, development,
and implementation of new systems
– Enables OLAP system to be divided into several
components that define its architecture
– OLAP is designed to meet ease-of-use as well
as system flexibility requirements
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OLAP Architecture
• Operational characteristics can be divided into
three main modules:
– Graphical user interface (GUI)
– Analytical processing logic
– Data-processing logic
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OLAP Architecture (cont’d.)
• Designed to use both operational and data
warehouse data
• In most implementations, data warehouse and
OLAP are interrelated and complementary
• OLAP systems merge data warehouse and
data mart approaches
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Relational OLAP
• Relational online analytical processing
(ROLAP) provides the following extensions:
– Multidimensional data schema support within the
RDBMS
– Data access language and query performance
optimized for multidimensional data
– Support for very large databases (VLDBs)
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Multidimensional OLAP
• Multidimensional online analytical
processing (MOLAP) extends OLAP
functionality to multidimensional database
management systems (MDBMSs)
– MDBMS end users visualize stored data as a 3D
data cube
– Data cubes can grow to n dimensions,
becoming hypercubes
– To speed access, data cubes are held in
memory in a cube cache
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Relational vs. Multidimensional OLAP
• Selection of one or the other depends on
evaluator’s vantage point
• Proper evaluation must include supported
hardware, compatibility with DBMS, etc.
• ROLAP and MOLAP vendors working toward
integration within unified framework
• Relational databases use star schema design
to handle multidimensional data
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Star Schema
• Data-modeling technique
– Maps multidimensional decision support data
into relational database
• Creates near equivalent of multidimensional
database schema from relational data
• Easily implemented model for multidimensional
data analysis while preserving relational
structures
• Four components: facts, dimensions, attributes,
and attribute hierarchies
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Facts
• Numeric measurements that represent specific
business aspect or activity
– Normally stored in fact table that is center of star
schema
• Fact table contains facts linked through their
dimensions
• Metrics are facts computed at run time
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Dimensions
• Qualifying characteristics provide additional
perspectives to a given fact
• Decision support data almost always viewed in
relation to other data
• Study facts via dimensions
• Dimensions stored in dimension tables
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Attributes
• Use to search, filter, and classify facts
• Dimensions provide descriptions of facts
through their attributes
• No mathematical limit to the number of
dimensions
• Slice and dice: focus on slices of the data
cube for more detailed analysis
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Attribute Hierarchies
• Provide top-down data organization
• Two purposes:
– Aggregation
– Drill-down/roll-up data analysis
• Determine how the data are extracted and
represented
• Stored in the DBMS’s data dictionary
• Used by OLAP tool to access warehouse
properly
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Star Schema Representation
• Facts and dimensions represented in physical
tables in data warehouse database
• Many fact rows related to each dimension row
– Primary key of fact table is a composite primary
key
– Fact table primary key formed by combining
foreign keys pointing to dimension tables
• Dimension tables are smaller than fact tables
• Each dimension record is related to thousands
of fact records
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Performance-Improving Techniques
for the Star Schema
• Four techniques to optimize data warehouse
design:
– Normalizing dimensional tables
– Maintaining multiple fact tables to represent
different aggregation levels
– Denormalizing fact tables
– Partitioning and replicating tables
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Performance-Improving Techniques
for the Star Schema (cont’d.)
• Dimension tables normalized to:
– Achieve semantic simplicity
– Facilitate end-user navigation through the
dimensions
• Denormalizing fact tables improves data access
performance and saves data storage space
• Partitioning splits table into subsets of rows or
columns
• Replication makes copy of table and places it in
different location
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Implementing a Data Warehouse
• Numerous constraints, including:
– Available funding
– Management’s view of role played by an IS
department
• Extent and depth of information requirements
– Corporate culture
• No single formula can describe perfect data
warehouse development
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The Data Warehouse as an Active
Decision Support Framework
• Data warehouse:
– Is not a static database
– Is a dynamic framework for decision support that
is always a work in progress
• Data warehouse is critical component of
modern BI environment
• Design and implementation must be examined
as part of entire infrastructure
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A Company-Wide Effort That Requires
User Involvement
• Data warehouse data cross departmental lines
and geographical boundaries
• Building a data warehouse requires the
designer to:
–
–
–
–
–
Involve end users in process
Secure end users’ commitment from beginning
Create continuous end-user feedback
Manage end-user expectations
Establish procedures for conflict resolution
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Satisfy the Trilogy:
Data, Analysis, and Users
• Data warehouse designer must satisfy:
– Data integration and loading criteria
– Data analysis capabilities with acceptable query
performance
– End-user data analysis needs
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Apply Database Design Procedures
• Company-wide effort requiring many resources
• Quantity of data requires latest hardware and
software
• Detailed procedures to orchestrate flow of data
from operational databases to data warehouse
• People with advanced database design,
software integration, and management skills
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Data Mining
• Data-mining tools do the following:
– Analyze data
– Uncover problems or opportunities hidden in
data relationships
– Form computer models based on their findings
– Use models to predict business behavior
• Requires minimal end-user intervention
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SQL Extensions for OLAP
• Proliferation of OLAP tools fostered
development of SQL extensions
• Many innovations have become part of
standard SQL
• All SQL commands will work in data warehouse
as expected
• Most queries include many data groupings and
aggregations over multiple columns
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The ROLLUP Extension
• Used with GROUP BY clause to generate
aggregates by different dimensions
• GROUP BY generates only one aggregate for
each new value combination of attributes
• ROLLUP extension enables subtotal for each
column listed except for the last one
– Last column gets grand total
• Order of column list important
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The CUBE Extension
• CUBE extension used with GROUP BY clause
to generate aggregates by listed columns
– Includes the last column
• Enables subtotal for each column in addition to
grand total for last column
• Useful when you want to compute all possible
subtotals within groupings
• Cross-tabulations are good candidates for
application of CUBE extension
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Materialized Views
• A dynamic table that contains SQL query
command to generate rows
– Also contains the actual rows
• Created the first time query is run and summary
rows are stored in table
• Automatically updated when base tables are
updated
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Summary
• Business intelligence generates information
used to support decision making
• BI covers a range of technologies, applications,
and functionalities
• Decision support systems were the precursor of
current generation BI systems
• Operational data not suited for decision support
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Summary (cont’d.)
• Four categories of requirements for decision
support DBMS:
–
–
–
–
Database schema
Data extraction and loading
End-user analytical interface
Database size requirements
• Data warehouse provides support for decision
making
– Usually read-only
– Optimized for data analysis, query processing
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Summary (cont’d.)
• OLAP systems have four main characteristics:
–
–
–
–
Use of multidimensional data analysis
Advanced database support
Easy-to-use end-user interfaces
Client/server architecture
• ROLAP provides OLAP functionality with
relational databases
• MOLAP provides OLAP functionality with
MDBMSs
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Summary (cont’d.)
• Star schema is a data-modeling technique
– Maps multidimensional decision support data
into a relational database
• Star schema has four components:
–
–
–
–
Facts
Dimensions
Attributes
Attribute hierarchies
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Summary (cont’d.)
• Four techniques optimize data warehouse
design:
–
–
–
–
Normalize dimensional tables
Maintain multiple fact tables
Denormalize fact tables
Partition and replicate tables
• Data mining automates analysis of operational
data
• SQL extensions support OLAP-type processing
and data generation
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